Method and system for determining relevant patient information

ABSTRACT

Media, methods, and systems facilitate identification and presentation of medical information that is relevant to a current condition of a patient. The relative prominence of the information is associated with the relative relevance of the information with the current condition. A weighted graph is used to determine the relevance.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application No.62/014,589 filed Jun. 19, 2014, the contents of which are incorporatedby reference in its entirety into the present disclosure.

BACKGROUND

The advent of electronic medical records and networking capabilities forsharing these records has greatly increased the amount of patientinformation available to caregivers. While this availability ofinformation has given caregivers more opportunity to recognize relevanttrends and connections in a patient's medical history, the sheer amountof information can sometimes cause relevant or vital information to behidden among less important records. Even when a caregiver is able toeffectively locate the vital information, precious time may be lostreviewing the patient's full medical history for relevant information.

SUMMARY OF THE DISCLOSURE

The presently disclosed embodiments may help to facilitate easier reviewof patient medical information by providing highly relevant information.Additionally, the embodiments may present relevant information moreprominently in accordance with the information's relevance to thecurrent condition of the patient.

In one embodiment, an example computer readable medium storesinstructions that a processing system may execute in order to performcertain functions. The functions include receiving data indicating apatient's identity and a current condition associated with the patient.The functions also include using a weighted graph to determine arelevance score representing the relevance, to the current condition, ofhistorical patient information associated with the patient. In theweighted graph, the current condition and the historical patientinformation are each represented by respective nodes.

In another embodiment, an example method includes receiving dataindicating a patient's identity and a current condition associated withthe patient. The method also includes using a weighted graph todetermine a relevance score representing the relevance, to the currentcondition, of historical patient information associated with thepatient. In the weighted graph, the current condition and the historicalpatient information are each represented by respective nodes.

In another embodiment, an example processing system includes aprocessor, a computer readable medium, and a communication interface.The processor is configured to receive data indicating a patient'sidentity and a current condition associated with the patient. Theprocessor is also configured to use a weighted graph to determine arelevance score representing the relevance, to the current condition, ofhistorical patient information associated with the patient. In theweighted graph, the current condition and the historical patientinformation are each represented by respective nodes.

The foregoing is a summary and thus by necessity containssimplifications, generalizations and omissions of detail. Consequently,those skilled in the art will appreciate that the summary isillustrative only and is not intended to be in any way limiting. Otheraspects, inventive features, and advantages of the devices and/orprocesses described herein, as defined by the claims, will becomeapparent in the detailed description set forth herein and taken inconjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic illustrating elements of an example network,according to an example embodiment.

FIG. 2 is a schematic illustrating elements of an example processingsystem according to an example embodiment.

FIG. 3 is a block diagram illustrating a method according to an exampleembodiment.

FIG. 4 is a block diagram illustrating received data structuresaccording to an example embodiment.

FIG. 5 is a block diagram representing a weighted graph according to anexample embodiment.

FIG. 6 is a block diagram representing a portion of a weighted graphaccording to an orthopedic example.

FIG. 7 is a table showing example weight and hierarchal data accordingto an exemplary embodiment.

FIG. 8 is a first example weighted graph that utilize group nodes.

FIG. 9 is a second example weighted graph that utilize group nodes.

DETAILED DESCRIPTION

Referring generally to the figures, systems and methods are describedherein for facilitating the identification and intelligent presentationof patient information that is relevant to a current patient condition.

The following disclosure is divided into three main sections. The firstsection discusses the devices and systems that can be used in an exampleembodiment. The second section discusses the techniques and methodsinvolved in an example embodiment. The third section discusses aparticular embodiment of the present systems/method relating toorthopedics. Although the section on example methods references elementsfrom the example system section, this is not intended to imply that theexample systems and methods must be used together. Rather, the examplemethods may be carried out using any suitable system or combination ofsystems and the described example systems may carry out procedures otherthan those outlined in the example methods.

I. Example System Architecture

Functions and procedures described herein may be executed according toany of several embodiments. For example, procedures may be performed byspecialized equipment that is designed to perform the particularfunctions. As another example, the functions may be performed bygeneral-use equipment that executes commands related to the procedures.As still another example, each function may be performed by a differentpiece of equipment with one piece of equipment serving as control orwith a separate control device. As a further example, procedures may bespecified as program instructions on a computer-readable medium.

FIG. 1 shows a networked system 100 according to an exemplaryembodiment. As shown, the system includes a processing system 102communicatively coupled to a set of remote and/or local devices. In someembodiments, like the network shown in FIG. 1, processing system 102 mayconnect to various output systems, such as display device 110 and server112. Processing system 102 may also connect with various informationinput devices, such as external storage (Medical Records 104), procedurestorage 106, and/or other external devices/systems 108. Communicativelinks are formed between each of the elements of system 100. Such linksmay be any type of communicative connection. For example, theconnections may be wired electrical connections, wireless connections,fiber-optic connections, air interfaces, or acoustic transmissionnetworks.

Processing system 102 may be any generalized computing device thatstores instructions for carrying out an exemplary process.Alternatively, processing system 102 may be a specialized computingdevice configured to perform the certain functions needed usinghardware. In still other embodiments, processing system 102 may be a setof various computing devices, either performing the same function oreach configured to perform a specific function. Processing system 102may typically include a non-transitory computer-readable medium,processor, and communication interfaces among other example components,as described with reference to FIG. 2.

Display device 110 may be any of various device types. Display device110 may be associated with one or more network locations on any ofvarious communication networks. For example, if display device 110 is acellular phone, then the network address may be a telephone number andthe notification system may access a landline or cellular telephonenetwork to contact display device 110. As another example, if displaydevice 110 is an Internet-enabled computing device, then the networklocation may be a registration node on an email, VoIP, or otherregistration server with which the notification system may communicateover various data links. Any presently known or future device capable ofthese functions may be used as display device 110. Some non-limitingexamples include e-readers, tablets, laptops, smartphones, video phones,televisions, desktop computers, PDAs, pagers, and/or fax machines.

As will be explained more below, processing system 102 may receivepatient data from various sources. Although FIG. 1 shows three maintypes of information sources, this illustration is not meant to belimiting. Medical information may be received in ways other than thoseexplicitly shown and described. As shown in FIG. 1, processing system102 may receive data from, and transfer data to external storage in theform of medical records and procedures. Medical records 104 may storepreviously determined health information for continually updatedinformation from sources not directly connected to processing system102. Procedures storage 106 may store instructions, data structures,conversion factors, and raw data necessary for preforming the presentfunctions. In some cases, procedures storage 106 may store the weightedgraph of conditions.

One example processing system (102) is shown in FIG. 2. As shown,processing system 102 includes a processor 202, a computer-readablemedium (CRM) 204, and communication interfaces 208, all connectedthrough system bus 210. Also as shown, program instructions 206 arestored on computer-readable medium 204. In the present disclosure, thisdevice may be seen as an embodiment of processing system 102.

Processor 202 may include any processor type capable of executingprogram instructions 206 in order to perform the functions describedherein. For example, processor 202 may be any general-purpose processor,specialized processing unit, or device containing processing elements.In some cases, multiple processing units may be connected and utilizedin combination to perform the various functions of processor 202.

CRM 204 may be any available media that can be accessed by processor 202and any other processing elements in device 102. By way of example, CRM204 may include RAM, ROM, EPROM, EEPROM, NAND-based flash memory,CD-ROM, Bluray, or other optical disk storage, magnetic disk storage orother magnetic storage devices, or any other medium that can be used tocarry or store desired program code in the form of program instructionsor data structures, and which can be executed by a processor. Wheninformation is transferred or provided over a network or anothercommunications connection (either hardwired, wireless, or a combinationof hardwired or wireless) to a machine, the machine properly views theconnection as a CRM. Thus, any such connection to a computing device orprocessor is properly termed a CRM. Combinations of the above are alsoincluded within the scope of computer-readable media.

Program instructions 206 may include, for example, instructions and datacapable of causing a processing unit, a general-purpose computer, aspecial-purpose computer, special-purpose processing machines, or remoteserver systems to perform a certain function or group of functions.

Communication interfaces 208 may include, for example, wirelesschipsets, antennae, wired ports, signal converters, communicationprotocols, and other hardware and software for interfacing with externalsystems. For example, device 102 may receive text, audio, executablecode, video, digital information or other data via communicationinterfaces 208 from remote data sources (e.g., remote servers, internetlocations, intranet locations, wireless data networks, etc.) or fromlocal media sources (e.g., external drives, memory cards, specializedinput systems, wired port connections, wireless terminals, etc.).Example communication networks include Public Switched Telephone Network(PSTN), Public Switched Data Network (PSDN), a short message service(SMS) network, a local-area network (LAN), a voice over IP (VoIP)network, a wide area networks (WAN), a virtual private network (VPN), acampus area network, and the Internet. An example network maycommunicate through wireless, wired, mechanical, and or opticalcommunication links. Many other communication networks may also besuitable for the embodiments discussed herein.

Communication interfaces 208 may include user-interfaces to facilitatereceiving user-input and user-commands into device 102 and outputtinginformation and prompts for presentation to a user. Although suchinterfaces typically connect with human users, user-interface 212 mayalternatively connect to automated, animal, or other non-human “users.”Additionally, while input and output are described herein as occurringwith a user present, user-interface 212 need not present information toany actual user in order for present functions to be performed.User-input may be received as, for instance, wireless/remote controlsignals, touch-screen input, actuation of buttons/switches, audio/speechinput, motion input, lack of interaction for a predefined time period,and/or other user-interface signals. Information may be presented to theuser as, for instance, video, images, audio signals, text, remote deviceoperation, mechanical signals, media file output, etc. In some cases,separate devices may be operated to facilitate user-interface functions.

An example system may also include a variety of devices or elementsother than those shown in FIG. 2. For example, device 102 may includevisual displays or audio output devices to present results of an exampleprocess. As another example, CRM 204 may store computer applications forspecific data-generation or data-processing functions. Other examplesare possible.

II. Example Methods

FIG. 3 illustrates a method 300 according to an example embodiment.Although the figure shows a specific order of method steps, the order ofthe steps may differ from what is depicted. Also, two or more steps maybe performed concurrently or with partial concurrence. Such variationsmay depend on the software and hardware systems chosen and the specificembodiment. All such variations are within the scope of the disclosure.Likewise, software implementations could be accomplished with standardprogramming techniques with rule-based logic and other logic toaccomplish the various connection steps, processing steps, comparisonsteps and decision steps.

A. Identifying and Presenting Relevant Information

As shown at block 302, method 300 includes receiving data representing apatient's identity and a current health condition for the patient. Acomputing device or system, such as system 102, may receive data from avariety of sources and the received data may include various types ofinformation. In some cases, data may be received from a single sourceall at once. In other cases, data may be received from several sourcesand/or over several receiving steps. Medical data may be received, forexample, via communication interfaces 208 from local or remote externalsources, such as sources 104, 106, and 108. In some cases, data may bereceived periodically. In other cases, the data may be received througha synchronous or asynchronous push operation from the external sourcesor pull operation from system 102. In some embodiments, the source ofobserved data may be the originator of such data (e.g., medical monitor,lab system, sensing device, input device, etc.).

FIG. 4 shows a particular example of a format in which patient data 402may be structured upon receipt. In particular, patient data 402 includesone variable or data structure that is indicative of patientidentification (ID) information 404 and a separate stored data structureindicative of a current condition 406 associated with the patient. Thestructure is merely a point of reference and should not be seen aslimiting as other data structure may be used.

Although the singular term “current condition” may be used throughoutthe present disclosure, current condition 406 may include multipleparticular conditions, diagnoses, and attributes of the patient and/or amedical visit of the patient. In some embodiments, a system thatreceives multiple current conditions for a patient may select a singlecondition as the primary concern for a medical visit. In otherembodiments, the system may combine the set of current conditions toform a condition group based on the set of current conditions. Thesystem may automatically perform either of these procedures based onstored instructions, or it may prompt a user for instructions on how todeal with the multiple current conditions. In some situations, theconditions related to the current visit (facility, caregiver, time/dayof visit, etc.) may be used as indicative of a “current condition” ofthe patient. For example, if the user wishes to access relevantinformation prior to receiving any reason for the visit, then the systemmay use this supplementary condition information in determiningrelevance.

In one example embodiment, other external devices 108 may include acomputer operated by a patient receiving team. When the patient checksin with the receiving team, receiving staff may ask the patient orattending personnel (EMTs, family, police, etc.) for patientidentification information 404 related to the patient (e.g., name,address, Social Security number, insurance information, primary carenetwork, birthdate, etc.) and information about the patient's currentcondition 406. Current condition 406 may be, for example, an explanationfrom the patient about the reason for their visit to the medicalfacility, a description of the patient's medical issues from attendingpersonnel, data obtained upon admittance of a patient (e.g., bloodpressure, pulse, weight, etc.), or any other information relevant to apatient's current condition. In some cases, the requested identificationinformation for the patient may be stored as the key (name) for thereceived data. In other cases, the identification information may beused to find the particular identifier that the system already uses toidentify the patient. In this way, any new information about the patientmay be associated with existing patient information, withoutunnecessarily revealing confidential medical information to medicalstaff or caregivers.

In some cases, current condition 406 of the patient may be inferred fromstored information on the system (e.g., in CRM 204) or in an externalmemory (e.g., in medical records 104). For example, the system mayalready have a stored reason for the patient's visit, as in the case ofa follow-up visit for a problem that a doctor has already diagnosed. Asanother example, the particular type of appointment may dictate acertain condition (e.g., an appointment with a specialist may beassociated with their specialty or an appointment for a lab test may beassociated with conditions that would require such tests.)

Although many of the example situations described herein relate to thesituation of a patient coming to a medical center while experiencing the“current condition,” the present methods may be equally applied to othersituations. For example, if a doctor receives the results of a patient'slab test, then this doctor would have a need to quickly locateinformation relevant to a condition indicated by the test results. Asanother example, if a researcher wishes to investigate a patient'smedical history with regard to a particular medical condition (eithercurrent or former), then current condition 406 may be the condition thatthe researcher is currently investigating. As a further example, apatient who is worried about a potential medical condition may wish toreview their own medical records in light of this potential condition(i.e., before receiving a prognosis).

Current condition 406 for the patient may represent any type of medicaldisease, injury, procedure, test result, complaint, anomaly, or otherhealth-related conditions. For example, some common medical diagnosesare listed in classification standards like the ICD-9, ICD-10, DSM-IV,EUROCAT, READ, and SNoMed-CT, which classify diseases and health-relatedconditions or problems. As another example, standard medical proceduresare listed in procedural classifications such as HCPCS, ICD-10-PCS,ICHI, and ICPC, among other examples. Other example standardclassifications include Nursing Intervention and Outcome Classification,LOINC, and MeSH terminology standard. In addition to the standardizedmedical conditions from industry-standard classifications, medicalprofessionals may diagnose or derive their own conditionclassifications, based on experience. For example, if a patient'smedical condition is either undiagnosed or unknown, the caregiver (orthe system) may collect the various characteristics of the condition(symptoms, lab results, similarities to other known conditions, etc.)and use these to define a derived current condition. In someembodiments, the term preexisting may refer to the fact that thecondition is either a former condition, a continuing condition, or a newcondition that is not current condition 406. The term preexisting shouldnot necessarily be limited to those medical conditions that the patientno longer experiences.

As shown at block 304, method 300 may include receiving historicalpatient information associated with the patient. As indicated by thedashed lines in FIG. 3, an example embodiment need not necessarilyinclude such receiving. In some cases, the historical patientinformation may be associated with a preexisting condition of thepatient. Like the current condition, a preexisting condition may be anytype of condition or characteristic of the patient that could berelevant to a health professional. In addition to preexistingconditions, patient information may also be associated with familymedical history, personal medical history, risk factors, genetic data,or demographic information. As with the current condition, thehistorical patient information may be received from a variety ofinternal or external sources. In some cases, the information may bepreviously stored at the processing system and need not be received.Historical information may represent any information that was collectedprior to the time of searching for relevant information. Therefore, if aphysician receives a patient's current height and blood pressurereadings that a nurse collected only minutes ago, this information mayeither factor into the current condition or the historical informationdepending on the needs or preferences of the user. In a typicalsituation, the historical patient information may be included in anoverall patient's medical history. For instance, FIG. 4 shows an exampleformat of the received medical history 408, including severalpreexisting conditions (labeled “CONDITION #1”-“CONDITION #5”), severalrisk factors (“RISK FACTOR 1”-“RISK FACTOR 3”), and demographicinformation (“DEMOGRAPHICS”.) In an example embodiment, the system maysequentially or simultaneously compare each piece of received of patientinformation to current condition 406 as will be described. Accordingly,the process described with respect to the single piece of historicalpatient information may be a single iteration of the larger process.Additionally, other medical information, such as family medicalhistories, current treatments, patient location, etc. may be receivedwith the patient records.

In some cases, the system may request the patient's medical history inresponse to receiving the patient ID and current condition. In othercases, the medical records may be periodically sent to the system. Instill other cases, the medical records may be received along with thecurrent condition and ID. In this last example, the received data 400 inFIG. 4 may be considered to be a single dataset. The received data insuch an implementation may be structured as a single list of conditions,with the current condition being listed with the other historicalpatient information.

Once the history is received, the system may divide the history intopotentially relevant pieces of information. The conditions may bedivided according to standard classifications, caregiver preferences,and/or semantic segmentation based on language cues in the history. Insome embodiments, the system may already maintain segmentations in themedical file that divide out different pieces of information. In orderto process the received information, the system may need to determinewhat condition each segment of information may indicate. For example,results of a blood test may be indicative of a certain disease, but thesystem may need to identify the disease before other procedures areperformed. In other embodiments, the system may only require indicationsof the symptoms/characteristics, precluding the need for identifying thecondition.

In some embodiments, the historical medical information may includeconditions that are not yet diagnosed, but with which other historicalinformation and current conditions are often associated. For example, ifa certain condition is highly relevant to several conditions that thepatient has, especially if the diagnosed conditions are rarelyco-present in patients that do not have the undiagnosed condition, thenthe system may present the condition as a potential preexistingcondition. Additionally, the undiagnosed condition may be presentedprominently in response to determining that diagnosed conditions arepart of a family of conditions with the undiagnosed condition. If acaregiver then accesses the potential condition, the system may presentthe connected conditions that seem to indicate the presence of theundiagnosed condition. Even if the patient does not have the undiagnosedcondition, the condition may be presented due to a preexisting high riskfor developing the undiagnosed condition. In this sense, the risk is thehistorical patient information, and the risk is highly related to anactual condition.

As shown at block 306, method 300 may include using a weighted graph todetermine the relevance of the historical patient information. A graphis a data structure, in this case accessible by a computing/processingsystem, in which data points, called “nodes,” are linked to one anotherthrough defined connections, called “edges.” In the weighted graph, eachof the edges is associated with a particular “weight” (e.g., acoefficient, additive constant, ratio, etc.) that represents someintrinsic quality of the data represented by the nodes. For example, ageographic map could be digitally represented by a weighted graph, withthe nodes representing locations, the edges representing routes betweenthe locations, and the weight of each edge indicating a distance of theroute. In the mapping example, an input to the weighted graph may be astarting and ending node (location) and an output may be the shortestdistance between the nodes.

In the present embodiments, each of the nodes in the weighted graphs mayrepresent a piece of medical information that may be associated with apatient and the edges may represent the relevance of one condition toanother. FIG. 5 shows a particular example of a weighted graph ofmedical conditions in accordance with an example embodiment. Theconditions, connections, and weights shown in FIG. 5 are examples. Inthe embodiment of FIG. 5, the weights are represented by coefficientvalues between “0” and “1.” In this arrangement, the relevance of onecondition to another may be close to “1” if the two conditions arehighly interdependent and close to “0” if the conditions are not relatedat all. Other weighting systems may be applied equally as well,including all integers, decimals, fractions, categories, etc. Althoughweighted graph 500 is an incomplete graph (i.e., there are nodes that donot connect directly), an example system may use a complete graph (i.e.,an edge and weight is assigned to every possible connection betweennodes). Additionally, although the weighted edges are shown asunidirectional, some weighted graphs may include directionally dependentedges (i.e., edges that only connect in a certain direction from onenode to another).

In a complete and directionally dependent weighted graph, every possibleconnection between nodes would include two edges, one for eachdirection, so that a weight may be assigned for each direction. Such aweighted and directional implementation allows the system to establish ahierarchy, or family-tree type relationship between differentconditions. For example, if the body is represented by a weighted graph,as will be explained in more detail hereinafter, then conditions relatedto the outer extremities may be considered descendent conditions tosimilar conditions to the inner extremities. In one embodiment, thesystem may treat a patient's injuries to their left shoulder, leftforearm, and left thumb as a family of conditions, in which the shoulderinjury is the “ancestor” to the forearm injury and the forearm injury isancestor to the thumb injury. The weighted directional graphs may beuseful in such a situation, because information related to previousancestor injuries may be more relevant to current descendent injuriesthan information related to the descendent injuries is to currentancestor injuries. For instance, if a forearm injury is the currentcondition, then, while both the shoulder and thumb injuries are probablyrelevant, the ancestor injury (the shoulder) may be more important forthe doctor to recognize to avoid exacerbating the preexisting condition.Similar hierarchical relationships may be formed with respect to manymedical conditions.

Current conditions, preexisting conditions, and other health informationmay be grouped in some cases to express particular relationships betweeninformation. For example, conditions, treatments, and procedures thatare diagnosed, prescribed, or performed by a particular caregiver may belinked to a node representing that caregiver. As another example,medical information from a single visit may be linked to a noderepresenting that visit. As still another example, medical visits thatoccur at one medical facility, or in one department at a facility, maybe linked to a node representing that facility or department.Connections between the group nodes may be made and weighted so thatthese groupings may also contribute to the relevance of the medicalinformation.

As a specific example, a doctor that has already had one or two visitswith a patient may wish to have information that was created at thosevisits be more relevant than information from visits with other doctors.As another example, if the current condition is related to a malady thatmost commonly occurs during the winter, then information associated withvisits during the winter may be deemed more relevant. As still anotherexample, if the caregiver has positive experiences with one of thedepartments that previously treated the patient, then the caregiver mayindicate that information from that department be more relevant thaninformation from other departments.

Within each grouping, different weights may be assigned to theconnections between the group node and the individual pieces ofinformation. Such differences in weighting may result from someinformation being indicated, for example, as more relevant or importantby the physician on duty. As another example, if a treatment prescribedby a certain doctor was actually suggested by a nurse, other staffer, orautomated system, then the system may be programmed to assign a lowerweighting to the connection between the caregiver node and thetreatment. In the case that an automated system offers potentialdiagnoses and/or recommends treatments, the system may automaticallytrack whether the diagnoses and treatments for a visit were offered bythe system or directly by the physician. In this way, the weights may beadjusted automatically, without requiring the physician to manuallyenter how a diagnosis was made. Other example situations reasons may beused for determining weights either among grouping nodes or betweeninformation nodes and grouping nodes.

Information other than just a weight and direction may also be conveyedthrough edges. For example, an edge that leads to a piece of diagnosisinformation may have a category or type of “diagnosis” associateddirectly with the connection. When the system is assessing the relevanceof the diagnosis information, the categories or types associated withthe various edges may be used to determine relevance scores. Forexample, a program may specify that only three diagnoses may bedisplayed as relevant information. In this way, various features of theweighted graph may be assessed automatically as the system moves throughthe weighted graph.

FIGS. 8 and 9 show example weighted graphs that utilize group nodes. Asshown in FIG. 8, graph 800 includes a set of group nodes 802, 804, 806,808, and 810 representing the current visit of a patient (802) andvarious other medical visits (804, 806, 808, 810), with weightedconnections between current visit 802 and the previous visits.Additionally as shown, visit nodes 804, 806, 808, and 810 of graph 800each include weighted connections to various pieces of medicalinformation (812-829) based on predefined criteria. In some cases, asystem may identify particular pieces of information from a graph likethat shown in FIG. 8. For example, if the system selects medicalinformation for which both the visit weight (the weight from the currentvisit to the visit with which the information is associated) and theinformation weight (from the visit to the information) are greater than0.4, then the program might select information 816, 817, 819, and 827.As another example, if the system identifies information for which theinformation weight is greater than 0.5 and for which the number ofavailable information nodes for the visit is less than 5, then thesystem might select information 813, 814, and 827 as relevant. In othercases, a system may utilize a graph like that illustrated in FIG. 8 toidentify visits of interest rather than specific information. Forexample, if a system selects visits with a visit weight greater than0.5, then the system might select visits 806, 808, and 810. As anotherexample, if the program selects visits for which the sum of weights forall information nodes associated with the visit is greater than 1.0,then the program might select visits 804 and 808.

As shown in FIG. 9, graph 900 includes a group node 902 connected toseveral information nodes (904-908) by directed weighted connections.For example, group node 902 could represent a physician that a patienthas seen, with each information node representing a piece of informationthat was recorded by the physician. In the example of FIG. 9, each edgealso has an identifier (e.g., a relationship between the nodes,description of the child node, etc.) associated with the connection.Various types of identifiers may be used to categorize the connectionsas an additional level of complexity for the graph. FIG. 9 shows anexample in which the identifiers describe the type of information thatis represented by each child node. In particular, information 904 and908 may describe medications prescribed to a patient, information 905may be a diagnosis given to the patient, and information 906 and 907 mayrepresent procedures performed on the patient. Therefore, if a system isidentifying medications with an information weight higher than 0.4, thenthe system may select information 904 as the only medication informationmeeting the criteria. Through a combination of weighting and categoricalor type edges, a graph may allow the system to search by both numericaland categorical variables.

Also as shown in FIG. 9, some pieces of information may be associatedwith more than one group node. For example, if a patient is prescribedthe same medication at each of several doctor visits, then group nodesrepresenting each of the doctor visits would have references to the sameinformation. In the particular example of FIG. 9, a second group node910 and a third group node 912 also attach to information 904-907. Insuch a situation, the system may use the connection patterns todetermine the relevance of the information. For example, a prescriptionor diagnosis that is repeated over several doctor visits or by severaldoctors may be an important or likely relevant information.

Programmatically, a weighted graph may be implemented in various ways.For example, as shown in FIGS. 5 and 6, a weighted graph may includenodes of information and pointers, with each pointer associated with aweight. Such an implementation may be based on abstract data types, suchas lists, maps, multimaps, trees, or graphs. Some computer-programmingdefinitions of “graph” may include a particular value/pointer structurethat is not necessary to the present embodiments. Accordingly, thepresent embodiments should not necessarily be limited to only this typeof implementation. In other embodiments, like that shown in FIG. 7, theweighted graph may be implemented as a list of information/conditionidentifiers and a respective list of weights for each connection betweenthe identifiers. Such an implementation may be based on tables,hashtables, arrays, sets, and/or multisets, in addition to any otherpresent or forthcoming data structures. In practice, a completetable-based weighted graph may include an n×n matrix of weights (where nis the number of nodes) with a weight assigned to each potentialconnection between elements. In incomplete graphs, fewer values may bepre-computed with modifications and calculations made when a healthcareprovider begins a patient exam with a review of the patient's currentcondition and historical patient information. In a non-limiting example,such a healthcare provider may be a nurse working with a patient beforean encounter with the patient's primary care provider. When thehealthcare provider inputs the new data, it may be used by the processorto complete or partially complete a previously incomplete graph for thepatient. In some cases, pre-computed graphs are stored for laterreference in, for example, a database. In this way, logic can be quicklyapplied to, for example, linked-graphs or other unchanged portions ofgraphs, potentially reducing computation time for a desired, morecomplete weighted graph to be used during the patient encounter.

In some cases, the weighted graph may store information regarding therelevance of information to particular caregivers that may receive therelevant information. For example, the placement of the currentcondition on the weighted graph may depend on the caregiver's identity.Such an embodiment may be implemented by assigning caregiver-specificnodes for each condition that relates to the caregiver. Alternatively,specific condition nodes (or whole weighted-graph sections) may beassigned to each caregiver. As another example, different weights may beapplied to connections in the weighted graph based on which caregiver isseeking relevant information. Such an implementation may use separateweighted graphs for one or a set of caregivers. It should be noted that,in an exemplary embodiment, a caregiver is presented with the relevantmedical information without necessarily seeking out the information.Hence, the identity of the caregiver “seeking information” would beassessed on the basis of which caregiver will be receiving theinformation. Additionally, the caregiver “identity” may be a specificpersonal identity (e.g., Dr. Smith), a particular specialty (e.g.,ID=Oncologist), a medical facility (e.g., ID=Doctor at Four PinesHospital), or any other identifier that the weighted graph may use toassess the relevance of patient information.

Although the information may be described as contained in a singleweighted graph, the actual graph may be implemented as multiple weightedgraphs or subgraphs containing some shared information. For example, oneweighted graph may represent the general comparative relevance ofvarious conditions, that is, the relevance for a general population ofpatients, without specific patient information. Such a general graph maybe coupled to a graph of medical information for a particular patient sothat each graph provides part of the connectivity between nodes.Additionally, patient information may be stored in multiple locationsand, therefore, may be represented in separate subgraphs. In practice,one or more general graphs may be preloaded onto an accessing device sothat the information contained therein may be accessed and combined withspecific patient information that is downloaded when a user activatesthe programming. Additionally, breaking the full patient graph intosmaller subgraphs may allow for more efficient access and storage,precluding the need to download the whole graph at once.

The combining of multiple information may be executed in various way.For example, the two weighted graphs may be connected so that nodesrepresenting the same information/condition may be used as a single nodewith separate edges representing the weighting values from each of thegraphs. In another implementation, the various weights that representthe connections between two particular nodes (i.e., different weightsfor the connection in each graph) may be aggregated (e.g., averaged,summed, multiplied, etc.) to produce single weight values for eachconnection. In yet another implementation, edges may be establishedbetween the nodes of the separate graphs in order to represent differentlevels of connectivity between similar or equivalent nodes in differentgraphs.

In order to determine a relevance score for the historical patientinformation with respect to the current condition, a system may input anumerical value into the weighted graph at the node representing thehistorical patient information and then calculate the effect of theinput value at the node representing the current condition. Forillustration, take the example of condition 502 (“Heart Attack”) as thecurrent condition, and conditions 504 (“High Blood Pressure”) and 506(“Broken Pelvis”) are the preexisting conditions. To determine therelevance of information related to each preexisting condition, thesystem inputs a value (1, for instance) at the nodes for conditions 504and 506 and calculates the effect of these inputs at the node forcondition 502. For the example weights shown, the result would be:Relevance(High BP)=input*weight=1*0.9=0.9Relevance(Broken Pelvis)=input*weight=1*0.29=0.29

Accordingly, for a current condition of a heart attack, informationregarding the patient's high blood pressure is significantly morerelevant than information regarding the broken bones in the patient'spelvis.

In some cases, the system may treat some pieces of historical patientinformation as having a higher importance, independent of the currentcondition. For example, a very mild or long-past injury to the shouldermay be given less importance than a severe or recent injury to thethumb. Functionally, the system may implement such importance values byaltering the numerical value that is input at the node associated withthe historical patient information. For instance, taking the example ofthe heart attack and the weighted graph of FIG. 5, assume hypotheticallythat the patient's high blood pressure is right on the borderlinebetween high and normal blood pressure. Assume also that the patient'sbroken pelvis is a recent and severe injury (i.e., severe enough to haveformed a blood clot in a major artery). In this case, a value of 1 maybe assigned to the high blood pressure, and a value of 4 may be assignedto the broken pelvis. With these new values, the relevance scoresbecome:Relevance(High BP)=input*weight=1*0.9=0.9Relevance(Broken Pelvis)=input*weight=4*0.29=1.16

Accordingly, a very mild high blood pressure may be less relevant to aheart attack than the patient's recent and/or severe pelvic injury. Inan embodiment, the system may either determine recentness of the injury(and adjust the input and/or weight values) at the time that therelevant information is requested. In addition, the system mayoccasionally adjust the weights automatically as time passes. As usedherein, “occasionally” may mean periodically or it may mean in responseto a certain occasion/event. In either interpretation, the system wouldbe programmed to automatically respond to pre-specified time periods orevents by updating the information in the graph.

In addition to the above example of the relevance of one piece ofinformation being based on whether the weighting achieves a thresholdscore, other criteria may be used to determine relevance. For example,the number of “children” nodes that a condition or piece of informationhas in the graph may be used as a criterion for determining therelevance. In an example embodiment, a certain relevance search mayrequire that a condition have more than three children nodes in order tobe considered for relevance. Other relationships between weights,connections, and inputs may be used. As another example, the weightsassociated with the children, edges, and/or parent nodes of a particularcondition may be combined to compare to a threshold. As a particularimplementation, a threshold for relevance could be that the sum of allchildren nodes for a piece of information must be greater than a certainamount in order to be considered relevant.

Although the above example includes direct links from the patientinformation to the current condition, the relevance score may becalculated for indirect movements as well. However, some complexity maybe added in such a case due to the possibility of multiple paths havingdifferent weights. For example, to find the relevance of a past kneesurgery on a current heart attack, a pathway may go through the nodesfor either High Blood Pressure, Smoker, or Broken Pelvis. To correct forthis issue, some embodiments may adjust the weights to be conservative(each path from one node to a second node has the same weight).Alternatively, the system may simply avoid determining a relevance scorefor historical patient information that does not connect directly to thecurrent condition. As another alternative, the system may test allshortest routes to determine which route is most relevant. In theparticular case, the relevance scores would be:Relevance(Knee Surgery)=input*weight1*weight2=1*0.12*0.90=0.11Relevance(Knee Surgery)=input*weight1*weight2=1*0.79*0.29=0.23Relevance(Knee Surgery)=input*weight1*weight2=1*0.06*0.82=0.05Therefore, the maximum relevance of the knee surgery would be 0.23.

As shown at block 308, method 300 may also include prominentlypresenting the historical patient information based on the relevancescore. Like block 304, the presenting of information, as represented atblock 308, is optional and need not be performed in an exemplaryembodiment. Presenting the patient information may take several forms.For example, the information may be included in a graphical userinterface (GUI) and output to a display, such as display 110. In such anexample, system 102 may generate the GUI internally and output thegenerated GUI to display 110 through an integral, local, remote, orindirect network. As another example, system 102 may output therelevance data to a separate system that designs and outputs the GUI. Insome cases, the relevant information and/or designed GUI may be storedin memory for retrieval by a caregiver. Display device 110 may not be asimple monitor or other basic display device. Rather, the system 102 mayoutput relevant information to any computing device, portable device,wearable device, audio presentation system (as an audio user-interfacerather than a GUI), collection of displays, or other displaysystem/device. Presenting may refer to storing, displaying, transmittingto external systems, or presenting via audio, touch, or symbolicrepresentation.

A system may use any of several ways to determine whether the relevancescore associated with a piece of historical patient information isrelevant enough to be presented, and how prominently the informationshould be presented. In one embodiment, a simple threshold relevancelevel may be assigned and compared to the relevance level of the one ormore pieces of patient information. In such a case, all information witha relevance score higher than the threshold may be presented. In orderto determine how prominently the information is presented, a system mayhave multiple predetermined thresholds, which each define a differentlevel of prominence for the presentation of information.

In another embodiment, a maximum and/or minimum number of informationelements to be presented may be defined. For example, a system maypre-assign a portion of a GUI for historical patient information andalso define a number of pieces of information, n, that may berepresented in the portion. In this case, the information may be rankedaccording to relevance score, and the top n pieces of information areselected regardless of whether the relevance scores exceed a threshold.

In some embodiments, a GUI or other medium of presentation may includeelements other than the relevant medical information. A system may, insome embodiments, assign a relevance value for the other information onthe presentation medium. Based on the assigned relevance of the otherinformation, the system may select the threshold(s) for determiningrelevance or the maximum/minimum number of conditions to present. Forexample, the additional information could be included alongside thepreexisting condition information, so that the most relevant informationmay be presented regardless of whether it is medical information orother information.

In addition to using a graph to score and present relevant information.A system may use the weighted graph in other ways to assist a caregiveror patient. For example, the system may allow a user to view and/ormodify the contents of the weighted graph itself. For example, aninterface may be provided to show some or all of the patient's weightedgraph (e.g., in the formats shown in FIGS. 4-7 or other forms) to auser. Then, the user may choose to modify parameters, weights, orinformation in the weighted graph directly in either the same or adifferent accessible interface. In this way, a caregiver can see what iscausing particular information to be presented frequently or a user cansee what information is being presented to his or her caregivers. Insome cases, the identity of the caregiver may influence the presentationof the historical patient information. As discussed above, one or morecaregiver identities may be used in the weighted graph for assessingrelevance of information. Alternatively, the system may use caregiveridentity to filter information for which relevance has already beendetermined in accordance with the weighted graph. As an examplesituation, if the caregiver is a specialist in some area of medicine,then the information from that specialty may be presented moreprominently than similarly relevant information form another field ofmedicine. As another example, the system may store a set ofuser-preferences that define a relative prominence for certaininformation according to the desired workflow outlined by the caregiver.Such user-preferences may be stored as a user-profile in the system.Facility-wide, department-wide, or team-wide preferences may also beassigned on a profile basis to members of each respective group. Ingeneral, the smaller-group rules would supersede the general or largegroup preferences.

B. Constructing and Adjusting the Weighted Graph

In order to use the weighted graph for determining relevance, theweighted graph may need to be constructed based on the relationshipsbetween medical conditions and other potentially relevant information.Such a process is often imprecise, because the actual causal linkbetween one condition and another is difficult to assess. However, thesystem may design the weighted graph intelligently based on experience,known medical principles, and continued adjustment. Regarding theexperience-based construction and the known medical links betweenconditions, these values may be initially assessed and evaluated by ahuman caregiver, and input into the system. In other cases, the systemmay include automated agents/crawlers programmed to navigate throughscholarly documents in search of terms representative of one of theconditions in the weighted graph. The agent may then use semantictext-recognition to detect connections between conditions. For example,if two conditions are mentioned in the same article with linking wordssuch as “causal” or “correlated” in close proximity to descriptions ofeach condition, then the agent may return a tally of one positivemention between the two conditions. The system may react to such atransmission by increasing the weight between the two conditions.

Aside from the known or experienced connections, the system may usecontinuing streams of data to adjust the base model to better fit realconnections between conditions. In an automated approach, such a processmay incorporate machine learning algorithms as known to those of skillin the art. One simple technique may be to increase the weight betweentwo conditions in response to determining that the conditions oftenoccur in the same patients. As a refinement to this simple technique, asystem may more-heavily respond to cases in which the conditions arediagnosed or treated in temporal proximity (i.e., both conditionsmanifested at roughly the same time). Additional importance may be givento the first condition if it rarely occurs in patients that do not havethe second condition. As discussed above with respect todescendent/ancestor relationships, the relative weight of ancestorcondition (either more central than or potentially causal of thedescendant condition) may be higher than that of a descendent condition.

Although the present descriptions relate generally to finding relevantinformation by experienced medical professionals, the system may also bebeneficial for non-professionals. For example, if a new physician ornon-physician is attempting to learn about particular conditions ortreatments associated with their own symptoms or the symptoms of anotherperson, the weighted graph may be used to locate and rank relevantinformation. For example, a new doctor attempting to diagnose a patientthat has information stored within the weighted graph may input varioussymptoms and/or lab tests to see which conditions may be associated withthe expressed symptoms or patient history. Since the system may beconstantly learning and gathering relevance scores from a variety ofsources, the new physician may find the most likely set of possibleconditions and treatments as rated and modified by other professionals.Hence, a hospital may train the new doctor by providing access to such aweighted graph system and letting the doctor learn as he works.Additionally, as the doctor assesses treatments, the program may bringup potential or likely side effects, reactions, and value of the varioustreatments so that the doctor is better prepared to answer patientquestions.

As another example use for the present embodiments, a researcher may usethe collected relevance information, from the weighted graph, torecognize and establish particular connections or trends relating to theconnected conditions. For example, if two different conditions oftencoincide, then, based on this data, the researcher could driveinvestigations towards determining a correlation or cause for theconnection between the conditions. As another example, if one physicianoften has patients that receive a certain rare diagnosis, then ahospital administrator may recognize this connection through relevanceprocessing and respond accordingly. Other examples may be used.

Another technique by which the weight of an edge may be adjusted is tomonitor user-interactions with provided relevance information. Asdiscussed above, a caregiver may be presented with information that thesystem deems relevant to a current condition. After presenting theinformation, the system may receive indications as to whether thecaregiver selected the presented items for further review or diagnosis.If the caregiver did select the presented information, then the systemmay adjust the weighted graph to increase the weight of the edge betweenthe selected information and the current condition for which thecaregiver was searching. Such a process may also be used for trainingthe original weighted graph, outside of any real medical situation. Forexample, a set of medical professionals may be presented with currentconditions and potentially relevant historical patient informationand/or potential diagnoses as part of a graph-building or graph-refiningprocess. Then, the professionals may rate (on a yes/no, continuous ordiscrete scale, etc.) the relevance of the presented information.

III. Example Embodiment: Orthopedics

This example is given for illustration and should not be seen aslimiting the scope of the disclosed embodiments.

In one example embodiment, the present methods, media, and systems maybe used for recognizing relevant orthopedic medical history inaccordance with current orthopedic conditions. The main structure ofinterest in orthopedics is the skeletal system and the joints,musculature, and ligaments that support the skeletal system.Accordingly, the structure of a weighted graph for determining relevantinformation for an orthopedic application may relate to the structure ofthe skeletal system. In particular, edges that connect nodesrepresenting physically connected bones/muscles may be weighted moreheavily than edges between nodes representing bones that are separatedfrom one another.

FIG. 6 shows an example portion 600 of a weighted graph that includessome elements for an orthopedic representation of a patient's left hand.As shown, the group may be represented by the general node 602, “LeftHand,” that connects with three less general nodes (“Left Thumb” 604,“Left Index Finger” 606, and “Left Middle Finger” 608), which in turnconnect to three more specific nodes (“Left CMC Joint” 610, “Left 2^(nd)Metacarpal” 612, and “Left 3^(rd) Metacarpal” 614). As shown, weightedgraph 600 is directional and incomplete. However, a complete version ofsuch a system is possible and indeed has been designed for a fullskeletal system by the present inventor. For each connection (edge) ingraph 600, a weight (e.g., weights 616, 618) is defined, representing arelative relationship between the anatomy represented by the nodesaround the edge. Because of the directional weighting, differentrelevance levels may be assessed depending on the directionality of therelevance determination. For example, weight 616 may be applied todetermine the relevance of patient information associated with node 602to a current condition at node 604, and weight 618 may be applied todetermine the relevance of patient information associated with node 604to a current condition at node 602.

As discussed above, nodes that represent appendages to the centralskeletal structure may be represented by descendant nodes in ahierarchal arrangement. Since an injury to the second metatarsal bonemay be generalized as an injury to the left index finger or as an injuryto the left hand, node 612 may be seen as a descendant of nodes 606 and602. Similar hierarchal relationships may be formed for nodesrepresenting each part of the body's skeletal system based on theconnectivity of the anatomy (e.g., phalanges connected to themetatarsals, metatarsals connected to the talus, etc.) Accordingly, afull hierarchy of skeletal components may be represented as a weightedgraph and used to determine relevance of a preexisting orthopediccondition on a current orthopedic condition.

As discussed in an earlier section, ancestor nodes may have a morerelevant effect on their descendant nodes than the descendant nodes haveon the ancestors. FIG. 6 shows such relationships through thedirectional weighting. In particular, the relevance of a left handinjury (ancestor node 602) on a left CMC joint injury (descendant node610) is shown to be:Relevance(L·Hand to L·CMC Joint)=weight616*weight620=(0.99)*(0.99)=0.98

In contrast, the relevance of a left CMC joint injury on the left handas a whole would be:Relevance(L·CMC Joint to L·Hand)=weight622*weight618=(0.88)*(0.88)=0.77.

In other embodiments, descendants may be weighted the same or evenhigher than ancestor nodes to capture particular relationships.

In addition to ancestor and descendent relationships, other hierarchalpositions may be defined throughout a skeletal system. For instance, anedge between the left thumb (node 604) and the left index finger (node606) may be defined as “adjacent,” because the body parts representedare connected but neither is more general or central than the other(i.e., neither is a true ancestor to the other). As another example, anedge between a left thumb and a right thumb may define each node as“contralateral” to the other, because the two thumbs are at the samehierarchical level, but oriented on the opposite side of the body. Asstill another example, a right hand node may be identified as a“contralateral ancestor” node to the right thumb, because the right handis contralateral to an ancestor of the left thumb.

FIG. 7 shows an example relevance chart for different anatomy related tothe “Left Arm.” As shown, different weights and relationship types maybe defined for each anatomical part with respect to the left arm.Although the weights shown may be preferable, other example values maybe used. In the example of FIG. 7, if a patient has a preexisting injuryto the left arm, and has a current injury to the right scapula, then therelevance of the preexisting injury may be assessed as 0.48 (importancevalue), where the importance value relates to the severity or recentnessof the preexisting injury.

In the subject description, the word “exemplary” is used to mean servingas an example, instance or illustration. Any embodiment or designdescribed herein as “exemplary” is not necessarily to be construed aspreferred or advantageous over other embodiments or designs. Rather, useof the word exemplary is intended to present concepts in a concretemanner. Accordingly, all such modifications are intended to be includedwithin the scope of the present disclosure. The order or sequence of anyprocess or method steps may be varied or re-sequenced according toalternative embodiments. Any means-plus-function clause is intended tocover the structures described herein as performing the recited functionand not only structural equivalents but also equivalent structures.Other substitutions, modifications, changes, and omissions may be madein the design, operating conditions, and arrangement of the preferredand other exemplary embodiments without departing from scope of thepresent disclosure or from the scope of the appended claims.

What is claimed is:
 1. A non-transitory computer-readable medium havingstored thereon instructions executable by a processing system to causethe processing system to perform functions comprising: receiving dataindicative of a patient identity of a patient and a current condition;in response to receiving the data indicative of the patient identity andthe current condition, transmitting a request for historical patientinformation, and wherein the historical patient information is receivedin response to the transmitted request; dividing the historical patientinformation into a plurality of classifications, wherein at least one ofthe plurality of classifications is representative of a past diagnosisof the patient; using a weighted graph unique to the patient todetermine a weight between the past diagnosis and the current condition;using the weight and an input value to determine a relevance score ofthe past diagnosis to the current condition; determining a level ofprominence with which to display the historical information based on therelevance score, wherein determining the level of prominence comprisescomparing the relevance score to a plurality of thresholds, and whereineach threshold of the plurality of thresholds defines a different levelof prominence for displaying the historical patient information; anddisplaying the historical patient information on a graphical userinterface in accordance with the determined level of prominence suchthat historical patient information satisfying a first threshold of theplurality of thresholds is displayed in a first manner and historicalpatient information satisfying a second threshold of the plurality ofthresholds is displayed in a second manner.
 2. The non-transitorycomputer-readable medium of claim 1, wherein the current conditioncomprises a primary concern for a medical visit of the patient.
 3. Thenon-transitory computer-readable medium of claim 1, wherein thefunctions further comprise: detecting a change in relevance of the pastdiagnosis to the current condition; and responsively modifying theweighted graph to change the weight in accordance with the detectedchange in relevance.
 4. The non-transitory computer-readable medium ofclaim 3, wherein detecting the change in relevance comprises detectinguser-selection of presented indicia, and wherein the weighted graph isresponsively modified to increase the weight between a previouscondition and the current condition.
 5. The non-transitorycomputer-readable medium of claim 1, wherein the functions furthercomprise identifying an undiagnosed condition that has a high relevancewith respect to (i) other preexisting conditions associated with thepatient and (ii) the current condition; and creating a second relevancescore between at least the undiagnosed condition and the currentcondition.
 6. The non-transitory computer-readable medium of claim 5,wherein the functions further comprise: detecting that the patient hasreceived a second past diagnosis associated with the undiagnosedcondition; and in response to the detecting, modifying the weightedgraph to increase the second relevance score.
 7. The non-transitorycomputer-readable medium of claim 1, wherein the weighted graph defines,for each respective connection between any two nodes in the weightedgraph, a single respective weight, and wherein edges in the weightedgraph are directionally dependent.
 8. The non-transitorycomputer-readable medium of claim 1, wherein the relevance scorecomprises an output value, at a node associated with the currentcondition in the weighted graph, resulting from an input value at a nodeassociated with the historical patient information in the weightedgraph.
 9. The non-transitory computer-readable medium of claim 1,wherein the functions further comprise determining the input value basedon one or more of a severity and a recentness associated with the pastdiagnosis.
 10. The non-transitory computer-readable medium of claim 1,wherein weight values of edges in the weighted graph are determined inaccordance with known medical relationships between medical conditions.11. The non-transitory computer-readable medium of claim 10, wherein theknown medical relationships comprise hierarchical connections, whereinthe hierarchical connections comprise ancestor connections, descendentconnections, and adjacent connections.
 12. The non-transitorycomputer-readable medium of claim 1, wherein the functions furthercomprise receiving a second input value from an input device, and inresponse to the receiving of the second input value, updating therelevance score based on the second input value and the weight.
 13. Thenon-transitory computer-readable medium of claim 1, wherein thefunctions further comprise: using the weighted graph unique to thepatient to determine a second weight between the past diagnosis and thecurrent condition; and using the weight, the second weight, and an inputvalue to determine the relevance score.
 14. The non-transitorycomputer-readable medium of claim 1, wherein the functions furthercomprise: receiving a caregiver identity; and determining the level ofprominence with which to display the historical information furtherbased on the caregiver identity.
 15. The non-transitorycomputer-readable medium of claim 1, wherein the functions furthercomprise: determining a source that established the past diagnosis ofthe patient; and automatically adjusting the weight between the pastdiagnosis and the current condition based on the source that establishedthe past diagnosis of the patient.
 16. A method comprising: receiving,at a processing system, data indicative of a patient identity of apatient and a current condition; receiving information indicative ofhistorical patient information associated with the patient identity,dividing the historical patient information into a plurality ofclassifications, wherein the current condition and plurality ofclassifications are each represented by respective nodes in a weightedgraph unique to the patient; the processing system using the weightedgraph to determine a weight, wherein the weight correlates to an edgebetween one of the plurality of classifications to the currentcondition; the processing system using the weight and an input value todetermine a relevance score, wherein input value correlates to arelationship between the plurality of classifications to the currentcondition; the processing system determining a level of prominence withwhich to display patient information based on the relevance score,wherein determining the level of prominence comprises comparing therelevance score to a plurality of thresholds, and wherein each thresholdof the plurality of thresholds defines a different level of prominencefor displaying the patient information; and the processing systemcausing display of at least a portion of the patient information on agraphical user interface in accordance with the determined level ofprominence such that patient information satisfying a first threshold ofthe plurality of thresholds is displayed in a first manner and patientinformation satisfying a second threshold of the plurality of thresholdsis displayed in a second manner.
 17. The method of claim 16, furthercomprising: detecting user-selection of the patient information on thegraphical user interface; and responsively modifying the weighted graphto increase the weight of the edge between one of the plurality ofclassifications and the current condition in accordance with thedetected user selection of the patient information.
 18. A processingsystem comprising: a computer-readable medium; a communication interfacecommunicatively coupled to the computer readable medium; and a processorcommunicatively coupled to the computer-readable medium and thecommunication interface, wherein the processor is configured to: receivedata indicative of a patient identity of a patient and a currentcondition; receive information indicative of historical patientinformation associated with the patient identity; divide the historicalpatient information into a plurality of classifications, wherein thecurrent condition and plurality of classifications are each representedby respective nodes in a weighted graph unique to the patient; use theweighted graph to determine a weight representing a relevance of one ofthe plurality of classifications to the current condition wherein theweight correlates to an edge between one of the plurality ofclassifications to the current condition; use the weight and an inputvalue to determine a relevance score, wherein the input value correlatesto a relationship between the one of the plurality of classifications;determine a level of prominence with which to display the plurality ofclassifications based on the relevance score, wherein determining thelevel of prominence comprises comparing the relevance score to aplurality of thresholds, and wherein each threshold of the plurality ofthresholds defines a different level of prominence for displaying theplurality of classifications; and present the plurality ofclassifications on a graphical user interface in accordance with thedetermined level of prominence such that classifications satisfying afirst threshold of the plurality of thresholds is displayed in a firstmanner and classifications satisfying a second threshold of theplurality of thresholds is displayed in a second manner.
 19. Theprocessing system of claim 18, wherein the processor is furtherconfigured to: detect user-selection of the presented indicia; andresponsively modify the weighted graph to increase the weight of an edgebetween the historical patient information and the current condition inaccordance with the detected user selection of the presented indicia.20. The processing system of claim 18, wherein the relevance scorecomprises an output value, at a node associated with the currentcondition in the weighted graph, resulting from an input value at a nodeassociated with the historical patient information in the weightedgraph.